Difference of Visual Information Metric Based on Entropy of Primitive

Image sparse representation is a method of efficient compression and coding of image signal in the process of digital image processing.Image after sparse representation,to enhance the transmission efficiency of the image signal.Entropy of Primitive(EoP)is a statistical representation of the sparse representation of the image,which indicates the probability of each base element.Based on the EoP,this paper presents an image quality evaluation method-Difference of Visual Information Metric(DVIM).The principle of this method is to evaluate the image quality with the difference between the original image and the distorted image.The comparative experiments between DVIM&PSNR&SSIM are carried out.It was found that there was a great improvement in the image quality evaluation of geometric changes.This method is an effective image quality evaluation method,which overcomes the weakness of other quality evaluation methods for geometrically changing images to a certain extent,and is more consistent with the subjective observation of the human eye.

Steganalysis of LSB Matching Using Characteristic Function Moment of Pixel Differences

Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.

On the Essential Conditions of Difference Information and Class Ratio Dispersion of Several Kinds of Data Processing Methods Reducing in Grey Predicting Modelling

In this paper, author presents the essential conditions of difference information and class ratio dispersion reducing by logarithm and root sequence. They also point out that although new data is in the range suitable of the model, the error after it returns to original state might be great.